SPECIAL SECTION: NUCLEAR WASTE WATER AND GAS |
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Research on the simulated dispersion of gaseous radionuclides: Validation and application for splitting puff dispersion model |
YANG Li1, ZHANG Yujie1, FANG Sheng2, SONG Jiayue1, LI Xinpeng1, CHEN Yixue1 |
1. School of Nuclear Science and Engineering, North China Electric Power University, Beijing 102206, China; 2. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China |
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Abstract [Objective] Local-scale atmospheric dispersion modeling of radionuclides is crucial for nuclear emergency response during the early phase. The Lagrangian puff dispersion model excels in accurately and rapidly reproducing radioactive fields at this scale by accounting for natural turbulence and integrating wind fields with spatial and temporal variations. Given that nuclear power plants (NPPs), especially Chinese NPPs, are often located in heterogeneous terrains, which lead to channeling and slope flows, puff splitting in the puff dispersion model is necessary to accurately represent the phenomenon of plume splitting and layer decoupling phenomena. Despite its importance, the threshold values for puff splitting have not been adequately studied. In addition, the complex terrain around NPP sites generates highly complicated flows, necessitating the use of a diagnostic wind field model coupled with the atmospheric dispersion model to improve the accuracy of dispersion simulations. [Methods] To further provide an effective atmospheric dispersion modeling and establish threshold values of puff splitting for the Lagrangian puff dispersion model, the local-scale Lagrangian splitting puff dispersion model (SPUFF) was developed and fully integrated with the California meteorological model (CALMET). Two local-scale dispersion simulations were conducted using the CALMET to drive the SPUFF: one against the Sanmen NPP wind tunnel experiments with east (E) and northeast (NE) wind directions and another to simulate the Fukushima Daiichi nuclear accident. These simulations aimed to validate SPUFF's performance and practicality. Furthermore, a comprehensive sensitivity analysis was performed to determine the credible range of horizontal threshold values for puff splitting. The dispersion results were evaluated using multiple statistical metrics: the fraction of simulations within a factor of two/five/ten of the observations (FAC2/5/10), fractional mean bias (FB), normalized mean-square error (NMSE), normalized absolute difference (NAD), and geometric mean bias (MG). [Results] Validation results indicated that plumes generated by SPUFF effectively covered the majority of measurement sites, with coverage rates reaching 99.60% and 97.54% in the E and NE directions, respectively. All four crucial statistical metrics for SPUFF met acceptable criteria (FAC2: 0.52, FB: -0.17; NMSE: 0.75, NAD: 0.31 in the E direction; FAC2: 0.48, FB: 0.37; NMSE: 1.28, NAD: 0.39 in the NE direction), indicating remarkable performance. Practical evaluations demonstrated that SPUFF can reproduce more measurements in the Futaba station compared to the Lagrangian particle model (LAPMOD). SPUFF also successfully captured the concentration peak effects resulting from the reactor events during the Fukushima nuclear accident. Sensitivity analysis suggested that applying no puff splitting module might be sufficient for complex terrains with constant meteorological conditions (constant wind fields). However, puff splitting becomes crucial in complex terrains with variable meteorological conditions. For local-scale dispersion scenarios involving NPPs, the recommended threshold values for puff splitting range between 700 m and 1 100 m. [Conclusions] This paper provides a comprehensive evaluation of the Lagrangian splitting puff dispersion model (SPUFF) and demonstrates its practical application. The results strongly indicate that SPUFF is a valuable tool for future nuclear emergency responses. Additionally, this paper proposes a credible range of threshold values for puff splitting, offering guidelines for applying the puff model in local-scale dispersion scenarios at NPP sites.
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Keywords
Lagrangian puff dispersion model
wind tunnel experiments
Fukushima nuclear accident
puff splitting
sensitivity analysis
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Issue Date: 22 November 2024
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[1] LE N B T, KORSAKISSOK I, MALLET V, et al. Uncertainty study on atmospheric dispersion simulations using meteorological ensembles with a Monte Carlo approach, applied to the Fukushima nuclear accident[J]. Atmospheric Environment: X, 2021, 10: 100112. [2] LI X P, SONG J Y, YANG L, et al. Source term inversion coupling kernel principal component analysis, whale optimization algorithm, and backpropagation neural networks (KPCA- WOA-BPNN) for complex dispersion scenarios[J]. Progress in Nuclear Energy, 2024, 171: 105171. [3] ZHUANG S H, FANG S, DONG X W, et al. Local atmospheric transport behaviors of representative radionuclides during the Fukushima accident: A 200-m-resolution cross-scale study from site to 20 km[J]. Journal of Environmental Radioactivity, 2023, 265: 107212. [4] SONG J Y, YANG L, LI H T, et al. Comparison of intelligent optimization algorithms in neural network model for nuclear accident source term evaluation[C]//Proceedings of the ASME 2023 International Conference on Environmental Remediation and Radioactive Waste Management. Stuttgart, Germany: American Society of Mechanical Engineers, 2023. [5] TERADA H, NAGAI H, TANAKA A, et al. Atmospheric-dispersion database system that can immediately provide calculation results for various source term and meteorological conditions[J]. Journal of Nuclear Science and Technology, 2020, 57(6): 745-754. [6] YANG L, FANG S, ZHUANG S H, et al. Atmospheric 137Cs dispersion following the Fukushima Daiichi nuclear accident: Local-scale simulations using CALMET and LAPMOD[J]. Annals of Nuclear Energy, 2024, 195: 110137. [7] 杨力, 王存友, 陈义学, 等. 四种拉格朗日粒子浓度计算方法的评估——箱式计数法、 高斯核、 均匀核和抛物线核[J]. 中国环境科学, 2023, 43(7): 3404-3415. YANG L, WANG C Y, CHEN Y X, et al. Evaluation of four Lagrangian particle concentration calculation methods-box counting, Gaussian kernel, uniform kernel and parabolic kernel[J]. China Environmental Science, 2023, 43(7): 3404-3415. (in Chinese) [8] SEKIYAMA T T, KAJINO M. Performance of a 250-m grid eulerian dispersion simulation evaluated at two coastal monitoring stations in the vicinity of the Fukushima Daiichi nuclear power plant[J]. Journal of the Meteorological Society of Japan. Ser. II, 2021, 99(4): 1089-1098. [9] 刘爱华, 蒯琳萍. 放射性核素大气弥散模式研究综述[J]. 气象与环境学报, 2011, 27(4): 59-65. LIU A H, KUAI L P. A review on radionuclides atmospheric dispersion modes[J]. Journal of Meteorology and Environment, 2011, 27(4): 59-65. (in Chinese) [10] BARBERO D, RIBSTEIN B, NIBART M, et al. Reduction of simulation times by application of a kernel method in a high-resolution Lagrangian particle dispersion model[J]. Air Quality, Atmosphere & Health, 2023: 1-13. [11] BELLASIO R, BIANCONI R, MOSCA S, et al. Formulation of the Lagrangian particle model (LAPMOD) and its evaluation against Kincaid SF6 and SO2 datasets[J]. Atmospheric Environment, 2017, 163: 87-98. [12] BELLASIO R, BIANCONI R, MOSCA S, et al. Incorporation of numerical plume rise algorithms in the Lagrangian particle model (LAPMOD) and validation against the Indianapolis and Kincaid datasets[J]. Atmosphere, 2018, 9(10): 404. [13] UL HAQ A, NADEEM Q, FAROOQ A, et al. Assessment of Lagrangian particle dispersion model (LAPMOD) through short range field tracer test in complex terrain[J]. Journal of environmental radioactivity, 2019, 205-206: 34-41. [14] LEELÖSSY Á, LAGZI I, KOVÁCS A, et al. A review of numerical models to predict the atmospheric dispersion of radionuclides[J]. Journal of Environmental Radioactivity, 2018, 182: 20-33. [15] VAN LEUKEN J P G, SWART A N, HAVELAAR A H, et al. Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock: A review to inform risk assessment studies[J]. Microbial Risk Analysis, 2016, 1: 19-39. [16] 郝琦. 基于MPI并行计算框架的核事故后果评价系统研发[D]. 北京: 华北电力大学(北京), 2022. HAO Q. Research and development of nuclear accident consequence evaluation system based on MPI parallel computing framework[D]. Beijing: North China Electric Power University (Beijing), 2022. (in Chinese) [17] 樊庆旭. 基于拉格朗日烟团模型的放射性核素扩散模拟研究[D]. 北京: 华北电力大学(北京), 2023. FAN Q X. Research on simulation of radionuclide dispersion based on Lagrange puff model[D]. Beijing: North China Electric Power University (Beijing), 2023. (in Chinese) [18] THYKIER-NIELSEN S, DEME S, MIKKELSEN T K. Description of the atmospheric dispersion module RIMPUFF: RODOS(WG2)-TN(98)-02[R]. Riso National Laboratory, PO Box, 1999, 49. [19] SCIRE J S, STRIMAITIS D G, YAMARTINO R J. A user's guide for the CALPUFF dispersion model[M]. 5th ed. Concord: Earth Tech, Inc., 2000. [20] 刘毅, 刘龙, 李王锋, 等. 石化园区规划大气环境风险模拟方法与案例[J]. 清华大学学报(自然科学版), 2015, 55(1): 80-86. LIU Y, LIU L, LI W F, et al. Modeling regional atmospheric risks of petrochemical park planning[J]. Journal of Tsinghua University (Science and Technology), 2015, 55(1): 80-86. (in Chinese) [21] DONG X W, ZHUANG S H, FANG S, et al. Multi-scenario validation of CALMET-RIMPUFF for local-scale atmospheric dispersion modeling around a nuclear powerplant site with complex topography[J]. Journal of Environmental Radioactivity, 2021, 229-230: 106547. [22] DONG X W, ZHUANG S H, FANG S, et al. Site-targeted evaluation of SWIFT-RIMPUFF for local-scale air dispersion modeling around Sanmen nuclear power plant based on multi-scenario wind tunnel experiments[J]. Annals of Nuclear Energy, 2021, 164: 108593. [23] LIU Y, LI H, SUN S D, et al. Enhanced air dispersion modelling at a typical Chinese nuclear power plant site: Coupling RIMPUFF with two advanced diagnostic wind models[J]. Journal of Environmental Radioactivity, 2017, 175-176: 94-104. [24] SCIRE J S, ROBE F R, FERNAU M E, et al. A user's guide for the CALMET meteorological model[M]. 5th ed. Concord: Earth Tech, 2000. [25] YANG L, SONG J Y, CHEN Y X, et al. Evaluation of multiple Lagrangian particle dispersion concentration calculation methods based on the Belgian field experiment: Coupling CALMET with LAPMOD[C]//Proceedings of the 30th International Conference on Nuclear Engineering (ICONE). Kyoto, Japan: The Japan Society of Mechanical Engineers, 2023: 1158. [26] JANICKE U, JANICKE L. A three-dimensional plume rise model for dry and wet plumes[J]. Atmospheric Environ- ment, 2001, 35(5): 877-890. [27] WEBSTER H N, THOMSON D J. Validation of a Lagrangian model plume rise scheme using the Kincaid data set[J]. Atmospheric Environment, 2002, 36(32): 5031-5042. [28] 国家技术监督局, 国家环境保护局. 制定地方大气污染物排放标准的技术方法: GB/T 3840—1991[S]. 北京: 中国标准出版社, 1991. The State Bureau of Quality and Technical Supervision, State Environmental Protection Administration. Technical methods for making local emission standards of air pollutants: GB/T 3840—1991[S]. Beijing: Standards Press of China, 1991. (in Chinese) [29] BAKLANOV A, SØRENSEN J H. Parameterisation of radionuclide deposition in atmospheric long-range transport modelling[J]. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 2001, 26(10): 787-799. [30] ZHANG L M, GONG S L, PADRO J, et al. A size-segregated particle dry deposition scheme for an atmospheric aerosol module[J]. Atmospheric Environment, 2001, 35(3): 549-560. [31] HANNA S R, HANSEN O R, DHARMAVARAM S. FLACS CFD air quality model performance evaluation with Kit Fox, MUST, Prairie Grass, and EMU observations[J]. Atmospheric Environment, 2004, 38(28): 4675-4687. [32] HANNA S, CHANG J. Acceptance criteria for urban dispersion model evaluation[J]. Meteorology and Atmospheric Physics, 2012, 116(3-4): 133-146. [33] WANG S T, LI X P, FANG S, et al. Validation and sensitivity study of Micro-SWIFT SPRAY against wind tunnel experiments for air dispersion modeling with both heterogeneous topography and complex building layouts[J]. Journal of Environmental Radioactivity, 2020, 222: 106341. [34] 刘蕴, 方晟, 李红, 等. 基于四维变分资料同化的核事故源项反演[J]. 清华大学学报(自然科学版), 2015, 55(1): 98-104. LIU Y, FANG S, LI H, et al. Source inversion in nuclear accidents based on 4D variational data assimilation[J]. Journal of Tsinghua University (Science and Technology), 2015, 55(1): 98-104. (in Chinese) [35] TOKYO ELECTRIC POWER COMPANY. On-site meteorological data from Fukushima Daiichi nuclear power plant[EB/OL]. (2012)[2024-02-13]. https://www.tepco.co.jp/decommission/data/monitoring/monitoring_post/index-j.html. [36] TSURUTA H, OURA Y, EBIHARA M, et al. Time-series analysis of atmospheric radiocesium at two SPM monitoring sites near the Fukushima Daiichi nuclear power plant just after the Fukushima accident on March 11, 2011[J]. Geochemical Journal, 2018, 52(2): 103-121. [37] GEOSPATIAL DATA CLOUD SITE, COMPUTER NETWORK INFORMATION CENTER, CHINESE ACADEMY OF SCIENCES. Terrain data[EB/OL]. (2015)[2024-02-13]. http://www.gscloud.cn/sources/details/421?pid=302. [38] EUROPEAN SPACE AGENCY (ESA). GlobCover land cover maps[EB/OL]. (2009)[2024-02-13]. http://due.esrin.esa.int/page_globcover.php. [39] KATATA G, CHINO M, KOBAYASHI T, et al. Detailed source term estimation of the atmospheric release for the Fukushima Daiichi nuclear power station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model[J]. Atmospheric Chemistry and Physics, 2015, 15(2): 1029-1070. |
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